设计并评估用于阿尔茨海默病及相关痴呆症早期诊断的对话代理
Designing and Evaluating a Conversational Agent for Early Diagnosis of Alzheimer's Disease and Related Dementias
AI总结：
本文设计并评估了基于大型语言模型的对话代理，用于获取阿尔茨海默病及相关痴呆症患者的相关叙述，以支持早期诊断。
摘要：
阿尔茨海默病及相关痴呆症（ADRD）的早期诊断对于及时干预至关重要，但大多数诊断都推迟到晚期阶段。尽管全面的患者叙述对于准确诊断至关重要，但以往的研究主要集中在筛查研究上，这些研究通过互动来分类认知状态，而不是支持诊断过程。我们设计了基于语音交互的对话代理，利用大型语言模型（LLMs）来从患者和见证人那里获取与ADRD相关的内容，并提供信息。我们通过对话分析、用户调查以及与盲审专家访谈的比较，评估了该代理。代理检测到的症状与专家识别的症状显示出有希望的一致性。用户赞赏该代理的耐心和系统性提问，这支持了参与和表达复杂、难以描述的经历。尽管这些发现表明对话代理有潜力作为结构化诊断支持工具，但在更大样本上进行验证以及评估其临床效用之前仍需进一步研究。
英文摘要：
Early diagnosis of Alzheimer's disease and related dementias (ADRD) is critical for timely intervention, yet most diagnoses are delayed until advanced stages. While comprehensive patient narratives are essential for accurate diagnosis, prior work has largely focused on screening studies that classify cognitive status from interactions rather than supporting the diagnostic process. We designed voice-interactive conversational agents, leveraging large language models (LLMs), to elicit narratives relevant to ADRD from patients and informants. We evaluated the agent with 30 adults with suspected ADRD through conversation analysis, user surveys, and analysis of symptom elicitation compared to blinded specialist interviews. Symptoms detected by the agent showed promising agreement with those identified by specialists. Users appreciated the agent's patience and systematic questioning, which supported engagement and expression of complex, hard-to-describe experiences. While these findings suggest potential for conversational agents as structured diagnostic support tools, further validation with larger samples and assessment of clinical utility is needed before deployment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
